Load-Balancing Strategy: Employing a Capsule Algorithm for Cutting Down Energy Consumption in Cloud Data Centers for Next Generation Wireless Systems

Per-user pricing is possible with cloud computing, a relatively new technology. It provides remote testing and commissioning services through the web, and it utilizes virtualization to make available computing resources. In order to host and store firm data, cloud computing relies on data centers. Data centers are made up of networked computers, cables, power supplies, and other components. Cloud data centers have always had to prioritise high performance over energy efficiency. The biggest obstacle is finding a happy medium between system performance and energy consumption, namely, lowering energy use without compromising system performance or service quality. These results were obtained using the PlanetLab dataset. In order to implement the strategy we recommend, it is crucial to get a complete picture of how energy is being consumed in the cloud. Using proper optimization criteria and guided by energy consumption models, this article offers the Capsule Significance Level of Energy Consumption (CSLEC) pattern, which demonstrates how to conserve more energy in cloud data centers. Capsule optimization's prediction phase F1-score of 96.7 percent and 97 percent data accuracy allow for more precise projections of future value.


Introduction
Cloud computing is an extension of grid, parallel, and distributed computing techniques [1]. To achieve cloud computing, it conveys an assortment of equipment administrations, framework administrations, stage administrations, program administrations, and capacity administrations over the Web. Clients of cloud computing can utilize it on-demand, pay for it on-demand, and scale it up and down easily. Data centers have grown in size as cloud services have grown in popularity, necessitating a considerable amount of energy consumption. Te authors pointed out in [2] that data centers consume 1.5% of the yearly control created within the assembled states, agreeing with insights from the US Division of Energy. China's data centers are projected to consume about the same amount of energy as the United States and have surpassed the Gorges' yearly power generation. Te estimation of energy consumption has become the most difcult challenge in today's data center, so reducing energy consumption is a pressing issue that needs to be addressed in cloud computing research. One of the most predominant ways of bringing down vitality utilization is virtual machine solidifcation. Te overload/underload location, virtual machine determination, virtual machine arrangement [3][4][5], and virtual machine relocation [6,7] are all cases of positive virtual machine combination. Virtual machine movement can take a long time, squander a part of assets, and meddle with the working of other virtual machines on the server, resulting in a decrease in framework execution. Virtual machine relocation too requires the utilization of additional organized capacity [8]. Te detailed descriptions of energy consumption architecture and data fow are given below in Figure 1.
Virtualization is an important method in data centers because it allows customers to share resources by using virtual machines (VMs). Each virtual machine is separated and utilized to run customer applications, with storage capacity, primary memory, CPU, I/O capabilities, and network bandwidth requirements [9]. Some of the important characteristics that promote cloud computing performance are physical machine consolidation, fault tolerance, and load balancing. Physical Machine (PM) consolidation occurs through Virtual Machine (VM) migration, which occurs when a virtual machine's requested resources are unavailable on the physical machine, causing the virtual machine to be relocated. Te VM is moved to another physical computer to meet the VM requirement [10]. Te suggested method forecasts the power of each VM before VM migration, and then VMs are migrated to certain PMs based on this prediction and resource availability. Te VM power prediction improves system availability, reduces infrastructure complexity, and lowers cloud providers' operational costs, allowing customers to pay less [11]. To manage operations faster and deliver more reliable services to clients, it is necessary to forecast the VM's power in advance. Power conservation can be achieved by using various machinelearning approaches to forecast power use. Tis machinelearning-based technique is used in this study to forecast virtual machine power consumption, enrich cloud computing infrastructure, and improve service for IT industries. Furthermore, the power consumption of virtual computers is forecasted before they are assigned to physical machines [12].
Te proposed technique is exceptionally good at fnding acceptable computer resources in unknown networks since it incorporates a great positive input instrument and a dispersed look strategy. Tis article has provided a user-experience-based procedure for fnding energy-saving virtual machines. Te strides roulette likelihood choice component guides and maintains a strategic distance from the calculation entering the basic information to untimely joining, viably decreasing vitality utilization, and accomplishing an adjustment between vitality utilization and client encounter by altering the pheromone and heuristic calculate upgrade strategies and characterizing the parameter administrative calculate. Cloud information centers ofer various benefts, including on-demand assets, elasticity, fexibility, portability, and calamity recuperation [13]. One of the most important aspects of the cloud worldview is adaptability, which enables an application to grow its asset requests at any time [14]. Instead of purchasing and controlling computing resources, it has become more common to rent hardware, software, and network resources. With an Internet connection, users can take advantage of the entire processing infrastructure. It can be used in a wide variety of contexts, including commercial management, academic research, hospital administration, manufacturing, marketing, and many more [15].
Te following is our contribution: (i) Using historical data, we investigate and analyze the energy use of the data center. Te results of this article are utilized to create a statistical model that links meteorological variables to energy use. (ii) To use the statistical model to create a forecast model that can predict the data center's energy usage based on the weather forecast. Te model is validated by comparing it to real-world resource usage data obtained using the capsule optimization technique. (iii) To provide data center operators with an energy consumption forecast technique that allows them to optimize their power distribution and energy consumption by providing estimations of their resource utilization.
Te structure of the paper is laid out underneath. Section 2 has literature from past inquiries about workload estimation and vitality utilization in a cloud information center. Te proposed framework for controlling utilization based on ML-based approaches is examined in Section 3. Section 4 depicts the proposed approach's performance evaluation and serves as a conclusion in Section 5.

Literature Review
In the context of cloud server energy consumption management, the background of research, such as CPU utilization forecasting and resource usage forecasting and management, is one of the most successful techniques for anticipating the future. Te amount of power required to run and cool down the devices in the cloud data center increases day by day, increasing cloud service providers' operational costs. For better performance of a complex function, power consumption prediction is utilized to estimate the nonlinear future value. In [16], the author discussed an adaptive threshold method, local regression, and robust local regression to evaluate overloaded servers in IaaS infrastructure based on CPU use. Te threshold is automatically changed   Energy-based scheduling and accounting of VM [25] Supervised learning methods

High in energy consumption
Simulator (self-designed) Energy-efcient VM allocation technique using interior search algorithm [26] Self-adaptive diferential evolution algorithm Maximum the power consumed by data centers Open-source cloud middleware Eucalyptus Exact allocation and migration algorithm [27] Adaptive selector neural network Power consumed by data centers has not reduced and low reduction in the rate of task rejection

Cloud hypervisor xen
Energy-saving VM migration [28] Linear regression No support to the heterogeneous environment and unstable QoS Scheduler is implemented Energy-aware resource allocation algorithm [29] Neural network SLA nonviolation, no control wastage, and given no scalability Cloud sim Energy-efcient dynamic resource management [30] Gradient boosting tree Reduction in control utilization with the nonviolation of SLA Cloud sim Computational Intelligence and Neuroscience based on previous data analysis and manipulation with estimators such as mean absolute deviation and interquartile range. Te author [17] focused on applying autoregressive linear prediction to anticipate network demand. In this strategy, the data samples utilized for training to discover the link between attributes were smaller using cross-validation and the black box method. Te author in [18] introduced a tree regression (TR)-based model to compute VM power usage. Te black box method is used to collect information on the VM and server features. For their prediction model, they used data as linear values. Te author discussed the linear regression approach for forecasting cloud service workload [19]. Tey also used the auto-scaling technique to lower the operational costs of virtual resources by scaling them both vertically and horizontally. Using NASA trace and Saskatchewan trace, we devised a self-adaptive diferential evolution algorithm to estimate the workload used by the cloud data center in [18]. Te author discussed ftness function, mutation, and crossover in this method, which they found to be superior to other approaches such as particle swarm optimization (PSO), genetic algorithm (GA), and others.
In [20], the author discussed three versatile models for high vitality utilization and infringement of service-level understandings. When selecting virtual machines from overburden to decrease vitality utilization, SLA infringement was taken into consideration. At the same time, the execution of cloud information centers can be ensured. In [21], the author discussed the models for diminishing the vitality utilization of portable cloud information centers amid periods when virtual machines are inadequate or overburdened. For virtual machine determination and energetic blending, the recommended versatile heuristic energy-aware calculation perceives the history of CPU usage, which diminishes add up to vitality utilization and improves beneft quality. Compared to the most existing research, in [22], the author explored two extra key variables while handling the challenges of cloud data center energy usage and SLA violations: (1) Examining the stability of the CPU consumption upper limit. (2) When picking the virtual machine of the overburden based on the CPU utilization expectation, the execution debasement time and SLA infringement are diminished. To decrease vitality utilization with negligible SLA taking a toll, a heuristic method is displayed to identify the least-squares relapse of the overburden and select the virtual machine from the overburden with the lowest utilization estimate. In [23], the author discussed an energyaware energetic virtual machine choice calculation proposed in [23] for the issue of virtual machine integration to coordinate virtual machines from overburdened or underloaded to upgrade vitality utilization and expand beneft quality. Tere are a few pieces of literature listed below in Table 1.
In [31], the author discussed the problems of reducing VM power usage and cloud vendor operational costs in a cloud setting. Tey used an ad-hoc framework for VM consolidation, but this method ignored VM requirements such as disc space, network bandwidth, and the time it took a VM to execute a task. Te author has suggested [32] the use of a radial basis function (RBF) neural network to examine the power of VM with normalized parameters that satisfy the correlation coefcient of VM's power. Tis method used a tiny amount of samples for training and testing data, resulting in a neural network that could not make an accurate prediction. In [33], the author used machine learning methods to estimate VM resource management in the cloud platform based on Azure workload parameters such as frst-party IaaS and thirdparty PaaS services. Te authors used the fast Fourier transform to determine the type of VM workload and the cumulative distribution function to produce the graphs for CPU, memory, CPU core utilization per VM, and VM lifetime. After each prediction, accumulate the results in the Dynamically Linked Library (DLL) and determine whether the forecast was worthwhile. In [34], the author used supervised learning algorithms to analyze the workload of VM to reduce its power consumption. Tey compiled a list of diferent scheduling strategies for reducing carbon dioxide emissions from a data center. Te prediction error was calculated using statistical metrics such as RMSE, R squared, and accuracy, which were calculated using an algorithm. Te recurrent neural network was used to forecast and manage resource allocation to a cloud server. Tey used time-delay neural network (TDNN) and regression approaches to compare the outcomes of the server workload prediction. In [35], an adaptive selector neural network was developed for selecting the strategy for active VM reduction, and the results were compared to those of linear regression. Te customer's Service Level Agreement (SLA) with the cloud service provider was also crucial to this strategy; however, SLAs are still not met when customer requirements change. Te contribution of this study is also found in the description of a load-balancing algorithm inspired by energy consumption patterns that shows how we may save more energy in cloud data centers by using appropriate optimization rules informed by our energy consumption models in the literature.

Proposed Methodology
Te points of interest of the proposed demonstration counting preprocessing step and demonstration portrayal are given in the following segment. Te proposed model and data fow are given in Figure 2. Tis work has been gathered from input requests from a user and includes data cleaning, data balancing, transformation, aggregation, and data normalisation in the data preprocessing steps of the proposed model.
Te estimation of energy consumption uses a machine learning data model, which has been included in a Capsule algorithm that drops out fully connected layers. Te crossentropy has been calculated using the Softmax layer. Te evaluation metrics are calculated using the proposed model and compare the accuracy of the model with the state-ofthe-art models (ant colony and random forest). Figure 3 depicts a visual representation of the data center.
Te request of a user has to supply in two ways request such as power path to IT and power to secondary support. 4 Computational Intelligence and Neuroscience Te data center consisted of an uninterruptible power supply (UPS) and a Paragon Development System (PDS), as well as cabling and cooling light conditions. Furthermore, the data request was transferred to the IT load. Tere are a few abbreviations given in Table 2.
Te data fow of the proposed capsule signifcance level of energy consumption (CSLEC) is given in the form of a few steps, which are described in Algorithm 1 and Figure 4.
Step 1: the operational module gets the machine's current working status within the cloud data middle and then performs state control on each host Step 2: we exchange the host's running status and virtual machine line state to the client encounter module and obtain the accessible assets based on the CPU use edge you set Step 3: within the virtual machine planning module, initialize the pheromone for accessible resources Step 4: we put all of the capsules on the accessible at random Step 5: the capsule chooses another by calculating the likelihood determination instrument based on the pheromone concentration, the heuristic fgure, and the alteration factor Step 6: in case the CSLEC algorithm completes the look, upgrade the neighborhood and worldwide pheromones; on the of chance that it does not, return to Step 5 Step 7: the framework produces the ideal assignment scenario when the number of initialization emphases is met; otherwise, it returns to Step 4 Step 8: we check to see if there are any virtual machines Te complete steps have been described in Algorithm 1. Te fowchart of the proposed CSLEC model is shown in Figure 4. Te data are entered into the host voltage system, where the information is controlled and the proft matrix is     Computational Intelligence and Neuroscience utilizes (MU). Te outright Euclidean separate (ED) of all the energetic PM at the same time determines the framework's power usage. Te load-balanced system with the reduced ED is thought to be better. When no assignment is run in the relevant PM, the PM is turned of. Te power efcient factor (EF) of each active node is calculated based on equation (2).
Fitness function � Min β 1 (q) + β 2 (MC) + β 3 (MU) . (1) Te frst component in equation (1) stands for control utilization (q), the moment term stands for movement taking a toll (MC), and the third term stands for memory utilization (MU). Te outright Euclidean separate (ED) of all the energetic PM at the same time decides the framework's control utilization. Te load-balanced framework with the diminished ED is thought to be way better. When no task is run within the signifcant PM, the PM is turned of. Condition is utilized to decide the control profciency fgure (EF) of each dynamic hub equation (3).
where i⟶ Memory resources. Vi⟶ Given resource utilization. VBest i ⟶Best utilization of resource i for power efciency in each physical node.
Power efciency at time t is calculated as follows in equation (4): System total power efciency is represented as Another consideration for the objective function is the cost of migration. When the number of motions increases, the MC of the VM expands. Te best load-balancing system should have the least amount of movement. Te MC of the entire cloud arrangement is calculated using the conditions provided in No of migration in VM s Total no of VM s .
Another aspect of the load-balancing target function is memory use. Memory is nothing more than a jumble. Te heap structure is honestly based on the VM's benefts for setting up the assignments from various customers. CPUs and memory storage are among the resources used by the VM. Te storage utilization of the entire cloud setup is calculated using conditional logic equation: In condition (1), the objective work of our investigation is indicated. In this paper, the overobjective work is getting to be minimized by utilizing the ACSO calculation.

Data Balancing.
Te class imbalance problem happens when the quantity of samples in distinct classes of a dataset is unequally distributed. Minority classes receive fewer samples than other target groups, whereas majority classes receive more samples [36]. Minority classes must be properly supplemented since they are crucial for extracting information from unbalanced datasets. A method for boosting the sample size of minority groups is the Synthetic Minority Oversampling Technique (SMOTE). Using this technique, new artifcial samples are produced next to existing samples and then arranged in a line. After that, samples from nearby minority groups are matched with them. Notably, the sample features in adjacent classes are unafected, permitting SMOTE to create tests that drop interior with the most dispersion. Te recently made counterfeit information, which is calculated and utilized, is It is a number between 0 and 1, where D i represents the number related to minority samples and is the closest neighbour. Te capacity to generate new samples close to minority class data is one of the SMOTE technique's most noticeable advantages over other resampling techniques. Tis strategy is less complex and simpler than other databalancing methods like cost-sensitive ones.

Feature
Transformation. In our suggested model, label encoding is employed to convert nominal properties into numeric ones that may be interpreted by neural networks. Label embedding takes into account a number between zero and n−1 for each sample with nominal properties [18]. Te reason for using this strategy is that it does not alter the data's dimensionality.

Data Aggregation.
It envelops methods that result in the creation of modern highlights by combining two or more existing features. In comparison to the frst highlights, the modern highlights must be able to specify the dataset's data more successfully and totally. Te proposed work employments information accumulation to decrease dimensional Computational Intelligence and Neuroscience whereas moreover expanding the value of highlights and information soundness [20].

Normalization.
Te suggested model normalises the input data using the Max-Min normalisation method. Tis method applies a linear change to the original data while preserving the correlation between them [13]. Te normalisation approach is employed because the relationship between independent variables and the correlation between data are important in the prediction stated in where Min(A) and Max(A) denote the feature's minimal and maximum values, respectively, and x denotes the feature's current value.

Proposed Model.
A capsule may be a collection of neurons whose movement vector speaks to the instantiation parameters of a specifc sort of substance, such as a protest or a question parcel [14]. To put it another way, capsules encapsulate in vector form all relevant information about the status of the feature they are detecting [18]. Since the capsule is a vector, the length of it is a probability of detection of a feature, which means that even if the detected object has rotated, the length of the vector will be the same (the probability still stays the same), but the vector direction will change in the direction of the change. For example, let's assume that the current capsule has detected a face within an input image with a probability of 0.9. When the face starts to change location across the image, the capsule's vector will change direction, which means that it still detects; however, the length will be the same. Tis is exactly the form of invariance, which is not the max-pool ofer in CNN.

Fully Connected (FC) Layer.
Fully linked layers in neural networks are ones where all of the inputs from one layer are connected to each enactment unit of the following layer. Most common machine learning models' fnal few layers are complete related layers that combine the data retrieved by earlier levels to produce the fnal result [15]. A "Fully Connected (FC)" layer is planned to profciently handle vector information. Te model's depth should be properly calculated. We used one layer of fully linked layers in this example, but a service provider can alter it to establish a balance between the target model's complexity and the complexity of the target model (better detection accuracy).

Dropout Layer.
To avoid overftting, the dropout layer is used. Dropout is a neural network regularization strategy that reduces recurrent learning between neurons. As a result, certain neurons are disregarded at random during the training process [18].

Classifcation Layer (Softmax).
In the last layer, Softmax is utilized to categorise the data. Te last classifcation layer of a neural network uses a nonlinear activation function called Softmax [5]. Softmax is calculated using equation (9), and the output values are normalized so that the sum of the values is one.
where k is the conventional exponential function applied to each element of the input vector and is the k-dimensional input vector. Te fraction's denominator guarantees that all output values are between 0 and 1. Te relevant class's score must be maximized in the next section.

Result and Discussion
Tis work has provided a comprehensive analysis of energysaving calculations based on the arrival, processing, and response time of the virtual server. Tere are two diferent factors: processor utilization and energy consumption. Te experimental evaluation is carried out using the Clouds toolkit. It is a common framework for simulating cloud computing systems on local devices [37]. Cloud components such as data centers, virtual machines, and resource provisioning limits can be simulated using the CloudSim toolkit. Also, for the experiment, choose a sample size of 100 tasks, which were initially distributed over fve virtual machines [38].

Evaluation Metrics.
Te proposed model is evaluated using the accuracy, precision, and recall metrics as given in equations (10)- (12), respectively, where "TP" and "TN" refer to correctly categorized true positive and true negative samples [39]. Positive and negative instances that have been erroneously categorized are also referred to as FP and FN: Recall � TP TP + FN . Table 3 compares the proposed model's accuracy to that of other current models.
Te accuracy in Table 3 shows the comparison of different models; the proposed capsule model shows better accuracy in comparison with other pretrained models. Tis work compares the four diferent models, such as LR, PSO, Capsule, and CNN, that achieved 70%, 76%, 85%, and 95% data accuracy, respectively [40]. Te proposed CSLEC data model has achieved 97% data accuracy, which is better than another model's accuracy. Also, depending on the number of parameters in the trainable stage, the time taken for the training of the proposed model shows a better time in comparison with the pretrained CNN model shown in Table 4. Te accuracy Table 3 shows the comparison of diferent models; the proposed capsule model shows better accuracy in comparison with other pretrained models. Also, depending on the number of parameters in the trainable stage, the time taken for the training of the proposed model shows better time in comparison with the pretrained CNN model shown in Table 4. Te proposed model has been using 25346 trainable parameters, and it has consumed 1577.87 ms of time. Tis work has compared the four diferent algorithms: LR, PSO, Capsule network [41], and CNN. Te LR algorithm has used 16896 trainable parameters and consumed 588.31 training time. In comparison with the LR model, the PSO model has been used with 17154 parameters and a training time consumption of 946.52 ms. Te other training networks, Capsule and CNN, used 20960 and 33410 trainable parameters, respectively, consuming 967.14 and 1702.43 seconds. Out of this basic model, our proposed model has been trained with a large number of data and a 1577.87 consumption rate. Table 5 shows the diference in energy consumption with the use of diferent servers in Watts (W). As the level of workload increases, the percentage value of the Hitachi TS10 increases and reaches a maximum of 86.2 watts.
Te electric energy consumption has been considered by Fujitsu M1, Fujitsu M3, Hitachi TS10, and Hitachi SS10 server capacities, which has been considered in the range of 0% to 100% workload, and maximum server capacity has been estimated by Hitachi TS10 server as 41 Table 5 show the comparison of diferent server capacities; the proposed capsule model shows better accuracy in comparison with other pretrained models [42]. Also, depending upon the number of parameters in the trainable stage, the time taken for the training of the proposed model shows a better time in comparison with the pretrained CNN model shown in Table 4. Table 5 shows the diference in energy consumption with the use of diferent servers in watts (W). As the level of workload increases, the percentage value of the Hitachi TS10 increases and reaches a maximum of 86.2 watts.
Te server capacity of this work is shown in Figure 5. Te Hitachi TS10 has shown maximum electric consumption. Tis work has been estimating the electric energy consumption using diferent four types of servers, such as the "Fujitsu M1," "Fujitsu M3," "Hitachi TS10," and "Hitachi SS10." Hitachi TS10 [30] has achieved the best energy consumption accuracy of 86.2%. Tis work has also observed that the Fujitsu M1, Fujitsu M3, and Hitachi TS10 do not provide better energy consumption in terms of watts. Te result of the capsule algorithm in the form of diferent tasks is shown in Table 6.   Computational Intelligence and Neuroscience Tis work used the task, arrival time, response time, processing time, and energy consumption estimated in the unit of ms. Te task has been divided into fve diferent terms, such as T0, T1, T2, T3, and T4. Te user request was sent, and the server scheduled it based on the arrival time. Te maximum arrival time for task T4 is 22 ms, which is better than other tasks in comparison with processing time. But task T3 takes less processing time in comparison with others and also utilizes the minimum processor with an energy consumption of 54.7. Te energy consumed by the considered servers is shown in the form of a graph in Figure 6.
Te experiment was double-checked using a larger number of tasks and virtual machines in this paper. Te recorded results are also compared in order to assess the research project [43]. Te PSO load balancer algorithm is used to parse the simulations, and the results are then logged. Te Firefy load balancer is then used to run the same simulations. Te fndings are analyzed using fxed characteristics such as CPU utilization, reaction time, and throughput [44]. Te usage times of both approaches are now calculated using the above-mentioned energy formula.   For both the PSO and Capsule algorithms, this yields the energy consumption parameter. Te information gathered is analyzed and compared with the algorithms of PSO and Capsule [45]. Te information gathered is analyzed and compared [46]. Figure 7 compares the LR, CNN, and PSO models with the proposed model in terms of memory utilization. Te proposed model has efciently provided the accuracy of memory utilization of 170000 nm. Te comparative study of the energy consumption is shown in Figure 8.
Tis project explored a few versions before settling on a few to graphically depict the status. Te processing usage of the Capsule load balancer is higher than that of the PSO load balancer [47]. Te fnal parameter for comparing the two algorithms is energy consumption, which is calculated using this utilization [48]. A Firefy load balancer's average response time is faster than a PSO load balancer's. As previously stated, the response time has a wide-ranging impact on energy use. As a result of the faster response time, less energy is consumed [49]. Te amount of energy consumed is calculated by employing an equation that utilizes a settled value for the greatest control that can be devoured when the machine is completely stacked and a foreordained value for the least control that will be devoured when the machine is nearly still [50].

Conclusion
In a cloud data center, energy consumption is a major concern. With the rise in requests and a broad selection of cloud computing, it is presently fundamental to preserve successful and efcient data center methodologies to meet the approaching demands with the slightest amount of assets. In this work, we compared the training parameters and training time of diferent models, such as CNN, PSO, and Capsule, with the proposed model of CSLEC. Te CSLEC model has been used with 25346 training parameters and 1.57 training minutes (ms). Te proposed model has achieved an accuracy of 97%. Te proposed CSLEC algorithm's mathematical explanation has been thoroughly explained. Te experimental outcomes are represented using a variety of measures. In comparison to the existing method, the proposed strategy has the briefest make span and employs the least amount of vitality. In the future, we will actualize our strategy in real time and place a greater emphasis on it. In addition, this work calculated the energy consumed to assess the performance of the Capsule Signifcance Level of Energy Consumption (CSLEC). Comparative evaluations uncover that the proposed strategy is more successful at optimizing the vitality utilization parameter than the Molecule Swarm optimization calculation. When compared to PSO, the energy consumption of CSLEC is 10-14% lower.

Data Availability
Te data used in this work are available at https://planetlab. cs.princeton.edu/datasets.html.

Conflicts of Interest
Te authors declare that they have no conficts of interest regarding the publication of this manuscript. Computational Intelligence and Neuroscience 11